Inventory optimization is a critical component of effective supply chain planning, enabling businesses to strike a delicate balance between meeting customer demand and minimizing excess working capital. This process allows companies to respond nimbly to changes in market conditions. Optimizing inventory levels for even a single item can quickly get complex when you move beyond demand and supply uncertainty and consider multiple networks, customer agreements, common parts, shelf life, and much more.
That complexity increases by an order of magnitude when it comes to holistically solving for your end-to-end supply chain network, going beyond individual items or locations. Multi-echelon Inventory Optimization (MEIO) takes managing inventory to the next level by optimizing stock levels across the entire value chain, at each location and form, from raw to work-in-process to finished goods.
Managing this complexity is a perfect use case for artificial intelligence (AI). Various forms of AI, such as machine learning, neural networks, knowledge graphs, and of course, generative AI (GenAI), can be leveraged to analyze and provide insights over vast amounts of data to augment and accelerate supply chain decision-making. These innovations are transforming processes for forward-thinking companies in ways that were unimaginable.
AI enables robust network risk pooling to aggregate demand across locations and fulfillment paths, across products and bills of material to address variability and uncertainty. MEIO combines cost optimization and inventory optimization to minimize safety stock while guaranteeing demand at each endpoint at specified service levels, providing a holistic optimization of inventory.
AI is used to quickly identify different data patterns and attributes to help predict demand across time horizons, derive statistical distributions and cleanse data, to augment and automate inventory optimization. AI also segments product/location combinations through optimized safety stock cost, product profit margin, required service level, and more.
While it’s technically possible to do these things without AI, it would require multiple spreadsheets, a large volume of data, and a lot of time(!), and the result would still not reach the level of detail that AI can achieve. The scale of this undertaking, where inventory levels at one warehouse or product impacts others, meant that until recently companies rarely, if ever, performed multi-echelon inventory optimization.
AI delivers powerful analytics that are intuitive, efficient and transparent, with advanced prescriptive and predictive features, to help guide, augment and automate processes to quickly solve real-world problems. Because of the scale, speed and complexity at which it can take in and analyze data, applying AI to multi-level inventory management means companies can:
- Quickly tackle complex decisions with many variables
- Consider large sets of data at greater level of detail
- Optimize working capital investments
- Improved fill rates and Reduced product waste
MEIO in Action: A Real-World Example
The power of AI in MEIO is clear in this case of a global flavor and spice manufacturer. The company handles a broad array of ingredients and products — individual spices such as cayenne pepper, cardamon and oregano — but also combines those into different blends, such as herbs de provence. Products are sold in different packages, such as jars, pouches, and bags, and each spice varies by expiration dates, sourcing, demand, how many blends they are used in, and more.
Given all those variables, the manufacturer needs to determine when it makes sense to carry more raw material, semi-finished goods or finished goods, and at which location. It must also consider things like lead times, transfer times among facilities, customer needs, and working capital to come up with the best plan.
AI can take in and optimize for all those variables, looking at different types of patterns across various time horizons and classifying data to model the entire network. It can identify opportunities for risk pooling, and find ways to balance inventory at each stocking location and SKU level by looking at all nodes across the supply chain and the variability at each node, such as cycle times, lead times, and WIP. The result has led to better utilization of raw materials, stronger collaboration with suppliers and customers, all resulting in improved service levels and margins.
The use of AI in MEIO means companies use working capital more efficiently, achieve higher fill rates, reduce waste, and empower planners to be more productive. Because it can manage this complexity so well, AI enables MEIO to be performed continuously, instead of rarely.
Working with a top supply chain planning software vendor with decades of expertise and a long track record in innovation, like John Galt Solutions, is essential to gain all the benefits of AI applications such as MEIO and beyond. Click here to learn more.
